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ICML
2009
IEEE
16 years 7 months ago
BoltzRank: learning to maximize expected ranking gain
Ranking a set of retrieved documents according to their relevance to a query is a popular problem in information retrieval. Methods that learn ranking functions are difficult to o...
Maksims Volkovs, Richard S. Zemel
UAI
2003
15 years 8 months ago
Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
Uri Nodelman, Christian R. Shelton, Daphne Koller
GEOINFORMATICA
1998
96views more  GEOINFORMATICA 1998»
15 years 6 months ago
Experiments with Learning Techniques for Spatial Model Enrichment and Line Generalization
The nature of map generalization may be non-uniform along the length of an individual line, requiring the application of methods that adapt to the local geometry and the geographi...
Corinne Plazanet, Nara Martini Bigolin, Anne Ruas
TIP
2011
255views more  TIP 2011»
15 years 1 months ago
Dictionary Learning for Stereo Image Representation
—One of the major challenges in multi-view imaging is the definition of a representation that reveals the intrinsic geometry of the visual information. Sparse image representati...
Ivana Tosic, Pascal Frossard
CVPR
2012
IEEE
13 years 9 months ago
Unsupervised learning of translation invariant occlusive components
We study unsupervised learning of occluding objects in images of visual scenes. The derived learning algorithm is based on a probabilistic generative model which parameterizes obj...
Zhenwen Dai, Jörg Lücke